{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c106181e",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# A short tutorial on USTC-Pickers (<3 min)\n",
    "* If any question, please feel free to contact me via email: `zhujun2316@mail.ustc.edu.cn`\n",
    "* Dependencies: Obspy, PyTorch, [SeisBench v0.3.0](https://github.com/seisbench/seisbench/releases/tag/v0.3.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1098a5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Load a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4606d474",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are using the picker located at ../model_list/v0.1/北京市.pt\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/50/tw568v5s4lb0yd1_myl2dj_r0000gn/T/ipykernel_90497/2876095227.py:20: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  torch.load(\n"
     ]
    }
   ],
   "source": [
    "import seisbench\n",
    "import seisbench.models as sbm\n",
    "import os\n",
    "import glob\n",
    "import torch\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "from config import (en2cn, model_list)\n",
    "sample_rate = 50\n",
    "# 为拾取的目标区域选定合适的picker，如Sichuan（四川）、CSES（实验场）、TP（青藏高原）\n",
    "location = input('Plase specify a region or province to pick phases, e.g. Beijing. 42 pickers are available in the subfolder \"%s\"'%os.path.join('USTC-Pickers', 'model_list', 'v0.1'))\n",
    "if location not in en2cn:\n",
    "    exit('The region you specified is not available. Plase choose a region or province from below--------\\n%s\\n-----------------------------------------------------------------------------------------------'%(', '.join(en2cn)))\n",
    "else:\n",
    "    model_save_path = glob.glob(os.path.join(model_list, '*'+en2cn[location]+'.pt'))[0]\n",
    "    print('You are using the picker located at %s\\n'%model_save_path)\n",
    "    # 模型初始化\n",
    "    picker = sbm.PhaseNet(sampling_rate=sample_rate)\n",
    "    # 加载模型\n",
    "    picker.load_state_dict(\n",
    "        torch.load(\n",
    "            model_save_path,\n",
    "            map_location=device).state_dict()\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16005010",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Read waveforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "261df044",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../test_data/mseed/**.mseed\n",
      "6 Trace(s) in Stream:\n",
      "CN.BJ..BHE | 2018-07-18T15:24:11.300000Z - 2018-07-18T15:27:11.280000Z | 50.0 Hz, 9000 samples\n",
      "CN.BJ..BHN | 2018-07-18T15:24:11.300000Z - 2018-07-18T15:27:11.280000Z | 50.0 Hz, 9000 samples\n",
      "CN.BJ..BHZ | 2018-07-18T15:24:11.300000Z - 2018-07-18T15:27:11.280000Z | 50.0 Hz, 9000 samples\n",
      "CN.SC..BHE | 2010-03-14T20:34:19.500000Z - 2010-03-14T20:37:19.480000Z | 50.0 Hz, 9000 samples\n",
      "CN.SC..BHN | 2010-03-14T20:34:19.500000Z - 2010-03-14T20:37:19.480000Z | 50.0 Hz, 9000 samples\n",
      "CN.SC..BHZ | 2010-03-14T20:34:19.500000Z - 2010-03-14T20:37:19.480000Z | 50.0 Hz, 9000 samples\n"
     ]
    }
   ],
   "source": [
    "from obspy import read\n",
    "from config import (sac, mseed)\n",
    "\n",
    "# Read the 3-component waveforms as input\n",
    "#inputfile = mseed.replace('Beijing', 'Sichuan')\n",
    "inputfile = mseed.replace('Beijing', '*')\n",
    "#inputfile = sac.replace('Beijing', 'Sichuan')\n",
    "#inputfile = sac.replace('Beijing', '*')\n",
    "print(inputfile)\n",
    "stream = read(inputfile)\n",
    "print(stream)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d40ae72",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Model response (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a096abdc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "response = picker.annotate(stream)\n",
    "# Plot the response\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time_diff = response[0].stats.starttime-stream[0].stats.starttime\n",
    "fig, ax = plt.subplots(2, 1, sharex=True)\n",
    "t0 = np.arange(stream[0].stats.npts)/sample_rate\n",
    "t1 = np.arange(response[0].stats.npts)/sample_rate + time_diff\n",
    "ax[0].plot(t0, np.array([x.data for x in stream[:3]]).transpose(), label=[x.stats.channel for x in stream[:3]])\n",
    "ax[1].plot(t1, np.array([x.data for x in response[1:3]]).transpose(), label=['P', 'S'])\n",
    "ax[1].set_xlabel('Time (s)')\n",
    "ax[1].set_ylim(0, 1)\n",
    "for j in ax:\n",
    "    j.legend()\n",
    "    j.set_xlim(0, t0.max()+1)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3077d818",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Retrieve the predicted picks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e370f53",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing picks\n",
      "CN.SC.\t2010-03-14T20:35:00.920000Z\tS\n",
      "CN.BJ.\t2018-07-18T15:25:01.920000Z\tS\n",
      "CN.SC.\t2010-03-14T20:34:55.260000Z\tP\n",
      "CN.BJ.\t2018-07-18T15:24:51.140000Z\tP\n",
      "{'P': {'trace': ['CN.SC.', 'CN.BJ.'], 'time': ['20100314T20:34:55.26', '20180718T15:24:51.14']}, 'S': {'trace': ['CN.SC.', 'CN.BJ.'], 'time': ['20100314T20:35:00.92', '20180718T15:25:01.92']}}\n"
     ]
    }
   ],
   "source": [
    "picks = picker.classify(stream, P_threshold=.3, S_threshold=.3)\n",
    "print('Printing picks')\n",
    "lines = {phase:{'trace':[],'time':[]} for phase in 'PS'}\n",
    "def format_utc(t):\n",
    "    return '%04d%02d%02dT%02d:%02d:%02d.%02d'%(t.year,t.month,t.day,t.hour,t.minute,t.second,t.microsecond/1e4)\n",
    "for pick in picks:\n",
    "    print(pick)\n",
    "    # Please ensure both the station and network code exist in the header of the SAC files\n",
    "    lines[pick.phase]['trace'].append(pick.trace_id)\n",
    "    lines[pick.phase]['time'].append(format_utc(pick.peak_time))\n",
    "print(lines)\n",
    "import json\n",
    "# save results as json\n",
    "with open('results/picks_demo.json','w') as fp:\n",
    "    json.dump(lines,fp)\n",
    "# read results as dict\n",
    "with open('results/picks_demo.json','r') as fp:\n",
    "    data = json.load(fp)"
   ]
  }
 ],
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